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import openai |
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import langchain |
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import os |
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import json |
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from pprint import pprint |
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import pinecone |
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import time |
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from langchain.chat_models import AzureChatOpenAI |
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from openai.embeddings_utils import get_embedding, cosine_similarity |
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import tiktoken |
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from langchain.vectorstores import Pinecone |
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from langchain.chains import RetrievalQA |
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from langchain.chains import ConversationalRetrievalChain, ConversationChain |
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from langchain.memory import ConversationBufferWindowMemory |
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from typing import Optional |
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import pandas as pd |
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import ast |
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from langchain.prompts.chat import ( |
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ChatPromptTemplate, |
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SystemMessagePromptTemplate, |
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AIMessagePromptTemplate, |
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HumanMessagePromptTemplate, |
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) |
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from langchain.llms import OpenAI |
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from langchain.chains import LLMChain |
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from langchain.prompts.few_shot import FewShotPromptTemplate |
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from langchain.chat_models import ChatOpenAI |
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from langchain.prompts import ChatPromptTemplate |
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from langchain.embeddings import OpenAIEmbeddings |
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from langchain.prompts import PromptTemplate |
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from langchain.retrievers import BM25Retriever, EnsembleRetriever |
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from langchain.vectorstores import FAISS |
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from tqdm.autonotebook import tqdm |
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from Utility import MasterLLM, intent_recognition, entity_recognition_main, entity_recognition_sub, describe, compare, RAG, set_vector_store, sentiment |
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openai.api_type = "azure" |
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openai.api_base = "https://di-sandbox-gpt4.openai.azure.com/" |
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openai.api_version = "2023-07-01-preview" |
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openai.api_key = "69ec3919a7314784be9c4f7414286fba" |
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os.environ['OPENAI_API_KEY']=openai.api_key |
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os.environ['OPENAI_API_BASE'] = openai.api_base |
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os.environ['OPENAI_API_VERSION'] = openai.api_version |
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os.environ['OPENAI_API_TYPE'] = openai.api_type |
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data = pd.read_csv(r"Combined_data_final_V2.csv") |
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DataList = data.JSON_Data.values.tolist() |
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session_memory = {} |
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entities = {} |
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data_json = pd.read_csv('Combined_data_final_V2.csv') |
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data = pd.read_csv('Combined_data_SBC.csv') |
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data.drop(columns=['ID'],inplace=True) |
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result = {} |
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for main_key, group_df in data.groupby('License Name'): |
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group_dict = group_df.to_dict(orient='records') |
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result[main_key] = group_dict |
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json_result = json.dumps(result, indent=2) |
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json_result = json_result.replace("\\n","") |
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json_result = json_result.replace("\\u2002","") |
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json_result = json_result.replace("\n","") |
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json_result = json_result.replace("/","") |
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data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) |
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LicenseName = data['License Name'].to_list() |
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License_Service = data.groupby('License Name')['Service Name'].apply(list).reset_index(name='Service Name') |
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PINECONE_API_KEY = '49e9d57f-ca7b-45d8-9fe5-b02db54b2dc7' |
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pinecone.init( |
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api_key=os.environ.get('PINECONE_API_KEY') or '49e9d57f-ca7b-45d8-9fe5-b02db54b2dc7', |
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environment=os.environ.get('PINECONE_ENVIRONMENT') or 'gcp-starter' |
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) |
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def final_output(UserPrompt): |
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final_json = {} |
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intent = intent_recognition(UserPrompt) |
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print('Intent') |
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print(intent) |
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print() |
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oldPrompt = UserPrompt |
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if len(list(entities.keys())) == 0: |
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PrevEntity = [] |
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else: |
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PrevEntity = entities[list(entities.keys())[-1]] |
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Prev_Entity = [i.strip() for i in PrevEntity] |
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Prev_Entity = list(set(Prev_Entity)) |
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print('Prev_Entity') |
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print(Prev_Entity) |
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print() |
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UserPrompt, Prev_Entity = sentiment(UserPrompt, Prev_Entity) |
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print('New Prev Entity') |
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print(Prev_Entity) |
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print('UserPrompt') |
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print(UserPrompt) |
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print() |
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entity_main = entity_recognition_main(UserPrompt, LicenseName) |
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entity_main = list(set(entity_main)) |
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print('Main Entity') |
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print(entity_main) |
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print() |
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entity_sub = entity_recognition_sub(UserPrompt, entity_main,License_Service) |
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print('Sub Entity') |
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print(entity_sub) |
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print() |
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entities[oldPrompt] = entity_main |
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for i in Prev_Entity: |
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entity_main.append(i) |
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entity = entity_main |
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entity = list(set(entity)) |
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print('Full Entity') |
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print(entity) |
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print() |
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try: |
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json_data = json.loads(json_result) |
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filtered_data = {key: json_data[key] for key in entity} |
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filtered_json = json.dumps(filtered_data, indent=2) |
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final_json = json.loads(filtered_json) |
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except: |
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final_json = {} |
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c = 0 |
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if intent in ['Transfer']: |
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print(intent) |
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print() |
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output = "I have shared our last conversation on your WhatsApp" |
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return output, intent |
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elif len(entity)>0: |
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for i in entity: |
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if i in LicenseName: |
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c+=1 |
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if c > 0 and c == len(entity): |
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if intent in ['Describe'] and len(entity) > 0: |
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print('Describe') |
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output = describe(entity, final_json, UserPrompt) |
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elif intent in ['Compare'] and len(entity) > 0: |
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print('Compare') |
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output = compare(entity, final_json, UserPrompt) |
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else: |
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output = RAG(UserPrompt, entity) |
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print("RAG1") |
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else: |
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output = RAG(UserPrompt, entity) |
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print("RAG2") |
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else: |
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output = RAG(UserPrompt, entity) |
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print("RAG3") |
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session_memory[oldPrompt] = output |
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return output, intent |
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UserPrompt = "Explain retail licenses" |
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pprint(final_output(UserPrompt)) |
